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|a UniSZA
|e rda
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|a QA76.9
|b .N67 2020
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|a QA76.9
|b .N67 2020
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|a Nor Saidah Abd Manan ,
|e author
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|a Rule ensemble classification for school children in Terengganu
|c Nor Saidah bt Abd Manan
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|c 2020
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|a xiii,162 leaves ;
|c 31cm.
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|a text
|2 rdacontent
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|a unmediated
|2 rdamedia
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|a volume
|2 rdacarrier
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|a Thesis (Degree of Master of Science)- Universiti Sultan Zainal Abidin,2020
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|a Includes bibliographical references(leaves 121-134)
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|a 1. Introduction -- 2. Literature review -- 3. Methodology -- 4. Results and discussion -- 5. Conclusions and future works
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|a Childhood obesity is proven to increase the risks of early-onset diabetes which leads to
cardiovascular risk during adulthood and premature mortality. Its prevalence is
increasing at an alarming rate worldwide and imposes an enormous financial burden to
the government to treat its related co-morbidities. There is no national survey carried
out to determine the prevalence and trend of childhood obesity in Malaysia especially
in rural states such as Terengganu. In this thesis, the data collection and classification
of childhood obesity among year six school children from two districts in Terengganu;
Besut and Kuala Terengganu are discussed. The 4,245 data were collected from two
main sources; National Physical Fitness Standard for Malaysian School Children
Assessment Program (SEGAK) and a set of distributed questionnaires. An integrated
and automated SEGAK data collection and analysis system is proposed in this thesis.
The system, which is known as Health Monitoring System (HEMS), is a web-based
system developed with an automated data preprocessing using a three-tier system
architecture. This thesis will also introduce the use of data mining method by proposing
the best classifier for the classification of childhood obesity among year six school
children in Terengganu. Various combinations of attribute evaluator and search method
for feature selection were used to identify significant factors that can be considered as
potential risks that may influence the childhood obesity. Combination of feature
selection methods are then tested on different classifiers namely BayesNet, Naive
Bayes, J48, IBk and SMO. Using majority voting, the classifiers was combined. Since
the result of multi classifier is not satisfied, a rule was applied. The result showed that
ensemble classification using Consistency with Genetic Search for feature selection
gives the highest accuracy either with or without rule with the percentage of 76.61 %
and 75.22%, respectively. This study proved that an accurate classification model using
ensemble classification can be used to classify the childhood obesity among school
children in Terengganu. Other than that, the potentialrisk factors of childhood obesity
can be listed based on the features that were selected using Consistency with Genetic
Search.
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|a Universiti Sultan Zainal Abidin
|x Dissertations
|v Faculty of Informatics and Computing
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|a Mathematics
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| 650 |
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|a Model theory
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|a Universiti Sultan Zainal Abidin .
|b Faculty of Informatics and Computing
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|a 1000180271
|b Thesis
|c Reference
|e Tembila Campus
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